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communities <- read_csv("data/communities.data")
## Rows: 1994 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (1): communityname
## dbl (127): state, county, community, fold, population, householdsize, racepc...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
communities <- setNames(communities, c("State","County","Community","CommunityName","Fold","Population","HouseholdSize","RacePctBlack","RacePctWhite","RacePctAsian","RacePctHisp","AgePct12t21","AgePct12t29","AgePct16t24","AgePct65up","NumbUrban","PctUrban","MedIncome","PctWWage","PctWFarmSelf","PctWInvInc","PctWSocSec","PctWPubAsst","PctWRetire","MedFamInc","PerCapInc","WhitePerCap","BlackPerCap","IndianPerCap","AsianPerCap","OtherPerCap","HispPerCap","NumUnderPov","PctPopUnderPov","PctLess9thGrade","PctNotHSGrad","PctBSorMore","PctUnemployed","PctEmploy","PctEmplManu","PctEmplProfServ","PctOccupManu","PctOccupMgmtProf","MalePctDivorce","MalePctNevMarr","FemalePctDiv","TotalPctDiv","PersPerFam","PctFam2Par","PctKids2Par","PctYoungKids2Par","PctTeen2Par","PctWorkMomYoungKids","PctWorkMom","NumIlleg","PctIlleg","NumImmig","PctImmigRecent","PctImmigRec5","PctImmigRec8","PctImmigRec10","PctRecentImmig","PctRecImmig5","PctRecImmig8","PctRecImmig10","PctSpeakEnglOnly","PctNotSpeakEnglWell","PctLargHouseFam","PctLargHouseOccup","PersPerOccupHous","PersPerOwnOccHous","PersPerRentOccHous","PctPersOwnOccup","PctPersDenseHous","PctHousLess3BR","MedNumBR","HousVacant","PctHousOccup","PctHousOwnOcc","PctVacantBoarded","PctVacMore6Mos","MedYrHousBuilt","PctHousNoPhone","PctWOFullPlumb","OwnOccLowQuart","OwnOccMedVal","OwnOccHiQuart","RentLowQ","RentMedian","RentHighQ","MedRent","MedRentPctHousInc","MedOwnCostPctInc","MedOwnCostPctIncNoMtg","NumInShelters","NumStreet","PctForeignBorn","PctBornSameState","PctSameHouse85","PctSameCity85","PctSameState85","LemasSwornFT","LemasSwFTPerPop","LemasSwFTFieldOps","LemasSwFTFieldPerPop","LemasTotalReq","LemasTotReqPerPop","PolicRepPerOffic","PolicPerPop","RacialMatchCommPol","PctPolicWhite","PctPolicBlack","PctPolicHisp","PctPolicAsian","PctPolicMinor","OfficAssgnDrugUnits","NumKindsDrugSeiz","PolicAveOTWorked","LandArea","PopDens","PctUsePubTrans","PolicCars","PolicOperBudg","LemasPctPolicOnPatr","LemasGangUnitDeploy","LemasPctOfficDrugUn","PolicBudgPerPop","ViolentCrimesPerPop"))
communities <- transform(communities,
                         State == as.integer(State),
                         County == as.integer(County),
                         Community == as.integer(Community))
communities$Region <- as.factor(ifelse(communities$State%in%c(8,22,24,32,34,35,42,44,51), "Northeast", ifelse(communities$State%in%c(16,17,18,19,25,26,28,30,37,39,46,55), "Midwest", ifelse(communities$State%in%c(1,5,9,10,11,12,20,21,23,27,36,40,45,47,48,52,54), "South", ifelse(communities$State%in%c(2,4,6,7,14,15,29,31,33,41,50,53,56), "West", "Territory")))))
communities <- relocate(communities, Region, .before="Fold")
communities <- transform(communities, Region = as.factor(Region))
head(communities)
##   State County Community       CommunityName    Region Fold Population
## 1     8     NA        NA        Lakewoodcity Northeast    1       0.19
## 2    53     NA        NA         Tukwilacity      West    1       0.00
## 3    24     NA        NA        Aberdeentown Northeast    1       0.00
## 4    34      5     81440 Willingborotownship Northeast    1       0.04
## 5    42     95      6096   Bethlehemtownship Northeast    1       0.01
## 6     6     NA        NA   SouthPasadenacity      West    1       0.02
##   HouseholdSize RacePctBlack RacePctWhite RacePctAsian RacePctHisp AgePct12t21
## 1          0.33         0.02         0.90         0.12        0.17        0.34
## 2          0.16         0.12         0.74         0.45        0.07        0.26
## 3          0.42         0.49         0.56         0.17        0.04        0.39
## 4          0.77         1.00         0.08         0.12        0.10        0.51
## 5          0.55         0.02         0.95         0.09        0.05        0.38
## 6          0.28         0.06         0.54         1.00        0.25        0.31
##   AgePct12t29 AgePct16t24 AgePct65up NumbUrban PctUrban MedIncome PctWWage
## 1        0.47        0.29       0.32      0.20      1.0      0.37     0.72
## 2        0.59        0.35       0.27      0.02      1.0      0.31     0.72
## 3        0.47        0.28       0.32      0.00      0.0      0.30     0.58
## 4        0.50        0.34       0.21      0.06      1.0      0.58     0.89
## 5        0.38        0.23       0.36      0.02      0.9      0.50     0.72
## 6        0.48        0.27       0.37      0.04      1.0      0.52     0.68
##   PctWFarmSelf PctWInvInc PctWSocSec PctWPubAsst PctWRetire MedFamInc PerCapInc
## 1         0.34       0.60       0.29        0.15       0.43      0.39      0.40
## 2         0.11       0.45       0.25        0.29       0.39      0.29      0.37
## 3         0.19       0.39       0.38        0.40       0.84      0.28      0.27
## 4         0.21       0.43       0.36        0.20       0.82      0.51      0.36
## 5         0.16       0.68       0.44        0.11       0.71      0.46      0.43
## 6         0.20       0.61       0.28        0.15       0.25      0.62      0.72
##   WhitePerCap BlackPerCap IndianPerCap AsianPerCap OtherPerCap HispPerCap
## 1        0.39        0.32         0.27        0.27        0.36       0.41
## 2        0.38        0.33         0.16        0.30        0.22       0.35
## 3        0.29        0.27         0.07        0.29        0.28       0.39
## 4        0.40        0.39         0.16        0.25        0.36       0.44
## 5        0.41        0.28         0.00        0.74        0.51       0.48
## 6        0.76        0.77         0.28        0.52        0.48       0.60
##   NumUnderPov PctPopUnderPov PctLess9thGrade PctNotHSGrad PctBSorMore
## 1        0.08           0.19            0.10         0.18        0.48
## 2        0.01           0.24            0.14         0.24        0.30
## 3        0.01           0.27            0.27         0.43        0.19
## 4        0.01           0.10            0.09         0.25        0.31
## 5        0.00           0.06            0.25         0.30        0.33
## 6        0.01           0.12            0.13         0.12        0.80
##   PctUnemployed PctEmploy PctEmplManu PctEmplProfServ PctOccupManu
## 1          0.27      0.68        0.23            0.41         0.25
## 2          0.27      0.73        0.57            0.15         0.42
## 3          0.36      0.58        0.32            0.29         0.49
## 4          0.33      0.71        0.36            0.45         0.37
## 5          0.12      0.65        0.67            0.38         0.42
## 6          0.10      0.65        0.19            0.77         0.06
##   PctOccupMgmtProf MalePctDivorce MalePctNevMarr FemalePctDiv TotalPctDiv
## 1             0.52           0.68           0.40         0.75        0.75
## 2             0.36           1.00           0.63         0.91        1.00
## 3             0.32           0.63           0.41         0.71        0.70
## 4             0.39           0.34           0.45         0.49        0.44
## 5             0.46           0.22           0.27         0.20        0.21
## 6             0.91           0.49           0.57         0.61        0.58
##   PersPerFam PctFam2Par PctKids2Par PctYoungKids2Par PctTeen2Par
## 1       0.35       0.55        0.59             0.61        0.56
## 2       0.29       0.43        0.47             0.60        0.39
## 3       0.45       0.42        0.44             0.43        0.43
## 4       0.75       0.65        0.54             0.83        0.65
## 5       0.51       0.91        0.91             0.89        0.85
## 6       0.44       0.62        0.69             0.87        0.53
##   PctWorkMomYoungKids PctWorkMom NumIlleg PctIlleg NumImmig PctImmigRecent
## 1                0.74       0.76     0.04     0.14     0.03           0.24
## 2                0.46       0.53     0.00     0.24     0.01           0.52
## 3                0.71       0.67     0.01     0.46     0.00           0.07
## 4                0.85       0.86     0.03     0.33     0.02           0.11
## 5                0.40       0.60     0.00     0.06     0.00           0.03
## 6                0.30       0.43     0.00     0.11     0.04           0.30
##   PctImmigRec5 PctImmigRec8 PctImmigRec10 PctRecentImmig PctRecImmig5
## 1         0.27         0.37          0.39           0.07         0.07
## 2         0.62         0.64          0.63           0.25         0.27
## 3         0.06         0.15          0.19           0.02         0.02
## 4         0.20         0.30          0.31           0.05         0.08
## 5         0.07         0.20          0.27           0.01         0.02
## 6         0.35         0.43          0.47           0.50         0.50
##   PctRecImmig8 PctRecImmig10 PctSpeakEnglOnly PctNotSpeakEnglWell
## 1         0.08          0.08             0.89                0.06
## 2         0.25          0.23             0.84                0.10
## 3         0.04          0.05             0.88                0.04
## 4         0.11          0.11             0.81                0.08
## 5         0.04          0.05             0.88                0.05
## 6         0.56          0.57             0.45                0.28
##   PctLargHouseFam PctLargHouseOccup PersPerOccupHous PersPerOwnOccHous
## 1            0.14              0.13             0.33              0.39
## 2            0.16              0.10             0.17              0.29
## 3            0.20              0.20             0.46              0.52
## 4            0.56              0.62             0.85              0.77
## 5            0.16              0.19             0.59              0.60
## 6            0.25              0.19             0.29              0.53
##   PersPerRentOccHous PctPersOwnOccup PctPersDenseHous PctHousLess3BR MedNumBR
## 1               0.28            0.55             0.09           0.51      0.5
## 2               0.17            0.26             0.20           0.82      0.0
## 3               0.43            0.42             0.15           0.51      0.5
## 4               1.00            0.94             0.12           0.01      0.5
## 5               0.37            0.89             0.02           0.19      0.5
## 6               0.18            0.39             0.26           0.73      0.0
##   HousVacant PctHousOccup PctHousOwnOcc PctVacantBoarded PctVacMore6Mos
## 1       0.21         0.71          0.52             0.05           0.26
## 2       0.02         0.79          0.24             0.02           0.25
## 3       0.01         0.86          0.41             0.29           0.30
## 4       0.01         0.97          0.96             0.60           0.47
## 5       0.01         0.89          0.87             0.04           0.55
## 6       0.02         0.84          0.30             0.16           0.28
##   MedYrHousBuilt PctHousNoPhone PctWOFullPlumb OwnOccLowQuart OwnOccMedVal
## 1           0.65           0.14           0.06           0.22         0.19
## 2           0.65           0.16           0.00           0.21         0.20
## 3           0.52           0.47           0.45           0.18         0.17
## 4           0.52           0.11           0.11           0.24         0.21
## 5           0.73           0.05           0.14           0.31         0.31
## 6           0.25           0.02           0.05           0.94         1.00
##   OwnOccHiQuart RentLowQ RentMedian RentHighQ MedRent MedRentPctHousInc
## 1          0.18     0.36       0.35      0.38    0.34              0.38
## 2          0.21     0.42       0.38      0.40    0.37              0.29
## 3          0.16     0.27       0.29      0.27    0.31              0.48
## 4          0.19     0.75       0.70      0.77    0.89              0.63
## 5          0.30     0.40       0.36      0.38    0.38              0.22
## 6          1.00     0.67       0.63      0.68    0.62              0.47
##   MedOwnCostPctInc MedOwnCostPctIncNoMtg NumInShelters NumStreet PctForeignBorn
## 1             0.46                  0.25          0.04         0           0.12
## 2             0.32                  0.18          0.00         0           0.21
## 3             0.39                  0.28          0.00         0           0.14
## 4             0.51                  0.47          0.00         0           0.19
## 5             0.51                  0.21          0.00         0           0.11
## 6             0.59                  0.11          0.00         0           0.70
##   PctBornSameState PctSameHouse85 PctSameCity85 PctSameState85 LemasSwornFT
## 1             0.42           0.50          0.51           0.64         0.03
## 2             0.50           0.34          0.60           0.52           NA
## 3             0.49           0.54          0.67           0.56           NA
## 4             0.30           0.73          0.64           0.65           NA
## 5             0.72           0.64          0.61           0.53           NA
## 6             0.42           0.49          0.73           0.64           NA
##   LemasSwFTPerPop LemasSwFTFieldOps LemasSwFTFieldPerPop LemasTotalReq
## 1            0.13              0.96                 0.17          0.06
## 2              NA                NA                   NA            NA
## 3              NA                NA                   NA            NA
## 4              NA                NA                   NA            NA
## 5              NA                NA                   NA            NA
## 6              NA                NA                   NA            NA
##   LemasTotReqPerPop PolicRepPerOffic PolicPerPop RacialMatchCommPol
## 1              0.18             0.44        0.13               0.94
## 2                NA               NA          NA                 NA
## 3                NA               NA          NA                 NA
## 4                NA               NA          NA                 NA
## 5                NA               NA          NA                 NA
## 6                NA               NA          NA                 NA
##   PctPolicWhite PctPolicBlack PctPolicHisp PctPolicAsian PctPolicMinor
## 1          0.93          0.03         0.07           0.1          0.07
## 2            NA            NA           NA            NA            NA
## 3            NA            NA           NA            NA            NA
## 4            NA            NA           NA            NA            NA
## 5            NA            NA           NA            NA            NA
## 6            NA            NA           NA            NA            NA
##   OfficAssgnDrugUnits NumKindsDrugSeiz PolicAveOTWorked LandArea PopDens
## 1                0.02             0.57             0.29     0.12    0.26
## 2                  NA               NA               NA     0.02    0.12
## 3                  NA               NA               NA     0.01    0.21
## 4                  NA               NA               NA     0.02    0.39
## 5                  NA               NA               NA     0.04    0.09
## 6                  NA               NA               NA     0.01    0.58
##   PctUsePubTrans PolicCars PolicOperBudg LemasPctPolicOnPatr
## 1           0.20      0.06          0.04                 0.9
## 2           0.45        NA            NA                  NA
## 3           0.02        NA            NA                  NA
## 4           0.28        NA            NA                  NA
## 5           0.02        NA            NA                  NA
## 6           0.10        NA            NA                  NA
##   LemasGangUnitDeploy LemasPctOfficDrugUn PolicBudgPerPop ViolentCrimesPerPop
## 1                 0.5                0.32            0.14                0.20
## 2                  NA                0.00              NA                0.67
## 3                  NA                0.00              NA                0.43
## 4                  NA                0.00              NA                0.12
## 5                  NA                0.00              NA                0.03
## 6                  NA                0.00              NA                0.14

Things to investigate: * RacePerCap & VCPP * Incomes/Poverty & VCPP * Education & VCPP * Employment & VCPP * Rent & VCPP * PolicPerPop & VCPP

Significant = 95% CIs have no overlap at VCPP = 0 and VCPP = 1 Not Sig = 95% CIs have some overlap at VCPP = 0 and VCPP = 1

## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

MedIncome - Significant MedFamInc - Significant PerCapInc - Significant PctPopUnderPov - Significant

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

PctLess9thGrade - Significant PctNotHSGrad - Significant PctBSorMore - Significant

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

PctEmploy - Significant PctEmplManu - NOT Sig PctEmplProfServ - Significant PctOccupMgmtProf - Significant

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

MedRentPctHousInc - Significant MedOwnCostPctInc - Significant MedOwnCostPctIncNoMtg - Significant NumInShelters + NumInStreet - Significant # i wish i had the variable of like, (numinshelters+numinstreet)/(population) but because it’s all normalized htat’s not possible

## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1675 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 1675 rows containing missing values or values outside the scale range
## (`geom_point()`).

## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1675 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 1675 rows containing missing values or values outside the scale range
## (`geom_point()`).

## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1675 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 1675 rows containing missing values or values outside the scale range
## (`geom_point()`).

## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 1675 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 1675 rows containing missing values or values outside the scale range
## (`geom_point()`).

LemasSwFTPerPop - Significant LemasTotReqPerPop - Significant RacialMatchCommPol - Significant PolicBudgPerPop - NOT Sig

communities$Region <- communities |> mutate(State = case_when(
  communities$State%in%c(8,22,24,32,34,35,42,44,51) ~ "Northeast",
  communities$State%in%c(16,17,18,19,25,26,28,30,37,39,46,55) ~ "Midwest",
  communities$State%in%c(1,5,9,10,11,12,20,21,23,27,36,40,45,47,48,52,54) ~ "South",
  communities$State%in%c(2,4,6,7,14,15,29,31,33,41,50,53,56) ~ "West",
  TRUE ~ "Territory"
))
us.region <- function(State) {
  if (State %in% c(8,22,24,32,34,35,42,44,51)) {
    return("Northeast")
  } else if (State %in% c(16,17,18,19,25,26,28,30,37,39,46,55)) {
    return("Midwest")
  } else if (State %in% c(1,5,9,10,11,12,20,21,23,27,36,40,45,47,48,52,54)) {
    return("South")
  } else if (State %in% c(2,4,6,7,14,15,29,31,33,41,50,53,56)) {
    return("West")
  } else {
    return("Territory")
  }
}
communities <- communities |>
  mutate(USRegion = sapply(State, us.region))
ggplot(data = communities,
       mapping = aes(x = ViolentCrimesPerPop)) +
  geom_histogram(binwidth = 0.05) +
  labs(
    x = "Violent Crimes Per Population",
    y = "Frequency",
    title = "Violent Crime in the USA",
    subtitle = "1990 & 1995 Data"
  )

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.